Source code for asreview.state.base

# Copyright 2019 The ASReview Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from abc import ABC, abstractmethod

import numpy as np

[docs]class BaseState(ABC): def __init__(self, state_fp, read_only=False): """Initialize State instance. state_fp: str Path to state file. read_only: bool Whether to open file in read only mode. """ self.state_fp = state_fp self.read_only = read_only self.restore(state_fp) def __enter__(self): return self def __exit__(self, *_, **__): self.close() def __str__(self): return str(self.to_dict())
[docs] @abstractmethod def set_labels(self, y): """Add/set labels to state If the labels do not exist, add it to the state. Arguments --------- y: np.array One dimensional integer numpy array with inclusion labels. """ raise NotImplementedError
[docs] @abstractmethod def set_final_labels(self, y): """Add/set final labels to state. If final_labels does not exist yet, add it. Arguments --------- y: np.array One dimensional integer numpy array with final inclusion labels. """ raise NotImplementedError
@abstractmethod def _add_as_data(self, as_data, feature_matrix=None): """Add properties from as_data to the state. Arguments --------- as_data: ASReviewData Data file from which the review is run. feature_matrix: np.ndarray, sklearn.sparse.csr_matrix Feature matrix computed by the feature extraction model. """ raise NotImplementedError
[docs] @abstractmethod def get_feature_matrix(self, data_hash): """Get feature matrix out of the state. Arguments --------- data_hash: str Hash of as_data object from which the matrix is derived. Returns ------- np.ndarray or sklearn.sparse.csr_matrix: Feature matrix as computed by the feature extraction model. """ raise NotImplementedError
[docs] @abstractmethod def get_current_queries(self): """Get the current queries made by the model. This is useful to get back exactly to the state it was in before shutting down a review. Returns ------- dict: The last known queries according to the state file. """ raise NotImplementedError
[docs] @abstractmethod def set_current_queries(self, current_queries): """Set the current queries made by the model. Arguments --------- current_queries: dict The last known queries, with {query_idx: query_method}. """ raise NotImplementedError
@property @abstractmethod def settings(self): """Get settings from state """ raise NotImplementedError
[docs] @abstractmethod def add_classification(self, idx, labels, methods, query_i): """Add training indices and their labels. Arguments --------- indices: list, np.array A list of indices used for training. labels: list A list of labels corresponding with the training indices. i: int The query number. """ raise NotImplementedError
[docs] @abstractmethod def add_proba(self, pool_idx, train_idx, proba, query_i): """Add inverse pool indices and their labels. Arguments --------- indices: list, np.array A list of indices used for unlabeled pool. pred: np.array Array of prediction probabilities for unlabeled pool. i: int The query number. """ raise NotImplementedError
[docs] def is_empty(self): """Check if state has no results. Returns ------- bool: True if empty. """ return self.n_queries() == 0
[docs] @abstractmethod def n_queries(self): """Number of queries saved in the state. Returns ------- int: Number of queries. """ raise NotImplementedError
[docs] @abstractmethod def get(self, variable, query_i=None, default=None, idx=None): """Get data from the state object. This is universal accessor method of the State classes. It can be used to get a variable from one specific query. In theory, it should get the whole data set if query_i=None, but this is not currently implemented in any of the States. Arguments --------- variable: str Name of the variable/data to get. Options are: label_idx, inclusions, label_methods, labels, final_labels, proba , train_idx, pool_idx. query_i: int Query number, should be between 0 and self.n_queries(). idx: int, np.array, list Indices to get in the returned array. """ raise NotImplementedError
[docs] @abstractmethod def delete_last_query(self): """Delete the last query from the state object.""" raise NotImplementedError
[docs] def startup_vals(self): """Get variables for reviewer to continue review. Returns ------- np.array: Current labels of dataset. np.array: Current training indices. dict: Dictionary containing the sources of the labels. query_i: Currenty query number (starting from 0). """ labels = self.get("labels") train_idx = [] query_src = {} for query_i in range(self.n_queries()): try: label_idx = self.get("label_idx", query_i) labelled = self.get("inclusions", query_i) label_methods = self.get("label_methods", query_i) except (KeyError, IndexError): continue for i, meth in enumerate(label_methods): if meth not in query_src: query_src[meth] = [] query_src[meth].append(label_idx[i]) labels[label_idx[i]] = labelled[i] train_idx.extend(label_idx) if query_i > 0: n_queries = self.n_queries() last_inclusions = None try: last_inclusions = self.get("inclusions", n_queries-1) except KeyError: last_inclusions = [] if last_inclusions is None: last_inclusions = [] query_i_classified = len(last_inclusions) else: query_i_classified = 0 train_idx = np.array(train_idx, startup_vals = { "labels": labels, "train_idx": np.unique(train_idx), "query_src": query_src, "query_i": query_i, "query_i_classified": query_i_classified, } return startup_vals
def review_state(self): startup = self.startup_vals() return (startup["labals"], startup["train_idx"], startup["query_src"], startup["query_i"]) @property def pred_proba(self): """Get last predicted probabilities.""" for query_i in reversed(range(self.n_queries())): try: proba = self.get("proba", query_i=query_i) if proba is not None: return proba except KeyError: pass return None
[docs] @abstractmethod def initialize_structure(self): """Create empty internal structure for state""" raise NotImplementedError
[docs] @abstractmethod def close(self): """Close the files opened by the state. Also sets the end time if not in read-only mode. """ raise NotImplementedError
[docs] @abstractmethod def save(self): """Save state to file. Arguments --------- fp: str The file path to export the results to. """ raise NotImplementedError
[docs] @abstractmethod def restore(self, fp): """Restore or create state from a state file. If the state file doesn't exist, creates and empty state that is ready for storage. Arguments --------- fp: str Path to file to restore/create. """ raise NotImplementedError
[docs] def to_dict(self): """Convert state to dictionary. Returns ------- dict: Dictionary with all relevant variables. """ state_dict = {} state_dict["settings"] = vars(self.settings) global_datasets = ["labels", "final_labels"] for dataset in global_datasets: try: state_dict[dataset] = self.get(dataset).tolist() except KeyError: pass query_datasets = [ "label_methods", "label_idx", "inclusions", "proba", "pool_idx", "train_idx"] state_dict["results"] = [] for query_i in range(self.n_queries()): state_dict["results"].append({}) for dataset in query_datasets: try: state_dict["results"][query_i][dataset] = self.get( dataset, query_i).tolist() except (KeyError, IndexError): pass return state_dict